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Hepatitis E virus is a common cause of acute viral hepatitis. We analyzed reports of hepatitis E outbreaks among forcibly displaced populations in sub-Saharan Africa during 2010-2020. Twelve independent outbreaks occurred, and >30,000 cases were reported. Transmission was attributed to poor sanitation and overcrowding.

We conducted a retrospective analysis of all reports in ProMED-mail that were initially classified as undiagnosed diseases during 2007-2018. We identified 371 cases reported in ProMED-mail; 34% were later diagnosed. ProMED-mail could be used to supplement other undiagnosed disease surveillance systems worldwide.

Background In order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on event-based surveillance (EBS) and indicator-based surveillance systems. Event-based surveillance systems are key components of early warning systems and focus on fast capturing of data to detect threat signals through channels other than traditional surveillance. In this study, we develop Natural Language Processing tools that can be used within EBS systems. In particular, we focus on information extraction techniques that enable digital surveillance to monitor Internet data and social media. Results We created an annotated Spanish corpus from ProMED-mail health reports regarding disease outbreaks in Latin America. The corpus has been used to train algorithms for two information extraction tasks: named entity recognition and relation extraction. The algorithms, based on deep learning and rules, have been applied to recognize diseases, hosts, and geographical locations where a disease is occurring, among other entities and relations. In addition, an in-depth analysis of micro-average F1 metrics shows the suitability of our approaches for both tasks. Conclusions The annotated corpus and algorithms presented could leverage the development of automated tools for extracting information from news and health reports written in Spanish. Moreover, this framework could be useful within EBS systems to support the early detection of Latin American disease outbreaks.

Background The international health emergency caused by the emergence of the SARS-CoV-2 virus demonstrated the expanding usefulness of multi-country disease outbreak information gathered through event-based surveillance (EBS) as an extension beyond the main purposes of early warning, alert, and response (EWAR). In this article, previous events of multi-country outbreaks from 2010–2019 were reviewed for how EBS, within an expanded sphere of Epidemic Intelligence (EI), may help to enhance the understanding of outbreaks for a more timely and nuanced, multiple-point trigger approach to health emergencies. Methods The public, open-source database of ProMed reports were reviewed for the date of first notification on major outbreaks of infectious diseases and then compared for subsequent dates of any new, exceptional epidemiological findings (novel host, settings, transmission characteristics) as a determining factor for prolonged, multi-country events later acknowledged on the WHO disease outbreak news (DON) website, or by peer-reviewed journal publication if no related DON information became available. Results During the preceding decade, there was an ongoing occurrence of unexpected outbreaks requiring new information about previously unknown pathogens, such as MERS-CoV, and longstanding threats from multiple neglected tropical diseases. During these international outbreaks, key scientific insights about new host species, viral persistence, occurrence of human-to-human spread, and transmission setting, became known over the course of the response. Conclusion The timeliness between initial alerts of early outbreak detection and key epidemiological evidence about the emerging threat reached far beyond the first warning for the global community. To improve on the best knowledge available for an immediate response, it is recommended that further gathering and documentation from event-based surveillance is engaged to create a more complete assessment for uncontrollable infectious disease outbreaks and epidemics. Enhanced EBS (through modern tools, e.g., Epidemic Intelligence from Open Sources (EIOS) are critical for timely detection and response to such events.

Sources describing the global burden of emerging diseases accurately are still limited. We reviewed coronavirus infections reported by ProMED and assessed the reliability of the data retrieved compared to published reports. We evaluated the effectiveness of ProMED as a source of epidemiological data on coronavirus. Using the keyword “coronavirus” in the ProMED search engine, we reviewed all the information from the reports and collected data using a structured form, including year, country, gender, occupation, the number of infected individuals, and the number of fatal cases. We identified 109 entries reported between February 29, 2000 and January 22, 2020. A total of 966 cases were reported, with death reported in 188 cases, suggesting an overall case fatality rate (CFR) of 19.5%. Of 70 cases for which the gender was reported, 47 (67.1%) were male. Most of the cases were reported from China, the United Arab Emirates, and Saudi Arabia, with reports from other countries, including imported cases in Europe and North America. Internet-based reporting systems such as ProMED are useful to gather information and synthesize knowledge on emerging infections. Although certain areas need to be improved, ProMED provided useful information about coronaviruses especially during outbreaks.

During the past 100 years, Rift Valley fever virus (RVFV), a mosquito-borne virus, has caused potentially lethal disease in livestock, and has been associated with significant economic losses and trade bans. Spillover to humans occurs and can be fatal. Here, we combined data on RVF disease in humans (22 countries) and animals (37 countries) from 1931 to 2020 with seroprevalence studies from 1950 to 2020 (n = 228) from publicly available databases and publications to draw a more complete picture of the past and current RVFV epidemiology. RVFV has spread from its original locus in Kenya throughout Africa and into the Arabian Peninsula. Throughout the study period seroprevalence increased in both humans and animals, suggesting potentially increased RVFV exposure. In 24 countries, animals or humans tested positive for RVFV antibodies even though outbreaks had never been reported there, suggesting RVFV transmission may well go unnoticed. Among ruminants, sheep were the most likely to be exposed during RVF outbreaks, but not during periods of cryptic spread. We discuss critical data gaps and highlight the need for detailed study descriptions, and long-term studies using a one health approach to further convert the patchwork of data to the tale of RFV epidemiology.

Emerging and re-emerging infectious diseases have been frequently reported in Brazil. The Program for Monitoring Emerging Diseases (ProMED) is a virtual system with expert curation for monitoring health events, including those occurring in Brazil. This study aimed to describe the ProMED as a complementary surveillance system for emerging infectious diseases in Brazil. It has a retrospective and descriptive design, and was conducted using ProMED-PORT reports that cited Brazil and were published from 1 January 2015, to 31 December 2020. In total, 220 new reports were identified during the study period. Most of these were published between January and June. Reports on humans were predominant (n = 177), and comprised 78 kinds of events, most of which were related to arboviruses. Reports on animals were the second most prevalent (n = 35), and encompassed 18 kinds of events, particularly yellow fever in non-human primates, rabies in different mammals, and sporotrichosis in felines. Six (2.7%) reports were related to humans and animals, while two (0.9%) were related to plants or the environment. Most reports were from Southeast and Northeast regions. ProMED identified leading emerging and reemerging infectious diseases in Brazil, serving as an information source for local and international health authorities.

Background This study aimed to evaluate associations among countries’ self-reported International Health Regulation 2005 (IHR 2005) capacity assessments and infectious disease control outcomes. Methods Countries’ self-reported assessments implemented by percentages as IHR Monitoring Tools (IHRMT) in 2016 and 2017 were used to represent national capacity regarding infectious disease control. WHO Disease Outbreak News and matched diseases reports on ProMED-mail were collected in 2016 to represent disease control outcomes of countries. Disease control outcomes were divided in good, normal and bad groups based on the development of outbreaks listed in the reports. The Human Development Index (HDI), density of physicians and nurses, health expenditure, number of arrivals of international tourists were also collected for control. Chi-square test and logistic regression were applied for analysis. Results A total of 907 cases occurred in 92 countries. For all diseases, cases occurring in high international travel volume countries presented twice the risk of having a bad disease control outcomes than cases occurring in low international travel volume countries (OR = 2.19 for IHR 2016, OR = 2.97 for IHR 2017). Cases occurring in low IHR average score countries had significant higher risk (OR = 7.83 for IHR 2016 and OR = 2.23 for IHR 2017) of having a bad disease control outcomes than countries with high IHR average scores. For only human diseases, cases occurring in high international travel volume countries presented twice the risk of having a bad disease control outcomes than cases occurring in low international travel volume countries for IHR 2017 (OR = 2.79). Cases occurring in low IHR average score countries had significant higher risk (OR = 11.16 for IHR 2016 and OR = 3.45 for IHR 2017) of having a bad disease control outcomes than countries with high IHR average scores. The HDI, health workforce density and total health expenditure were all positively associated with disease control outcomes. Conclusions Countries’ self-reported infectious disease control capacities positively correlated with their disease control outcomes. While the self-reported IHR scores were accountable to some degree, this approach was useful for understanding global capacity in infectious disease control and in allocating resources for future preparedness.

Background The United Nations Refugee Agency (UNHCR) estimates the number of forcibly displaced people increased from 22.7 million people in 1996 to 67.7 million people in 2016. Human mobility is associated with the introduction of infectious disease pathogens. The aim of this study was to describe the range of pathogens in forcibly displaced populations over time using an informal event monitoring system. Methods We conducted a retrospective analysis of ProMED, a digital disease monitoring system, to identify reports of outbreak events involving forcibly displaced populations between 1996 and 2016. Number of outbreak events per year was tabulated. Each record was assessed to determine outbreak location, pathogen, origin of persons implicated in the outbreak, and suspected versus confirmed case counts. Results One hundred twenty-eight independent outbreak events involving forcibly displaced populations were identified. Over 840,000 confirmed or suspected cases of infectious diseases such as measles, cholera, cutaneous leishmaniasis, dengue, and others were reported in 48 destination countries/territories. The average rate of outbreak events concerning forcibly displaced persons per total number of reports published on ProMED per year increased over time. The majority of outbreak events (63%) were due to acquisition of disease in the destination country. Conclusion This study found that reports of outbreak events involving forcibly displaced populations have increased in ProMED. The events and outbreaks detected in this retrospective review underscore the importance of capturing displaced populations in surveillance systems for rapid detection and response.

Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)-mail is the most comprehensive international expert-curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the reports makes analysis for further application difficult. To make monitoring the epidemic situation in Southeast Asia more efficient, this study aims to develop an automatic summary of the alert articles from ProMED-mail, a huge textual data source. In this paper, we proposed a text summarization method that uses natural language processing technology to automatically extract important sentences from alert articles in ProMED-mail emails to generate summaries. Using our method, we can quickly capture crucial information to help make important decisions regarding epidemic surveillance. Our data, which span a period from 1994 to 2019, come from the ProMED-mail website. We analyzed the collected data to establish a unique Taiwan dengue corpus that was validated with professionals' annotations to achieve almost perfect agreement (Cohen κ=90%). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long short-term memory with attention mechanism with infused latent syntactic features to identify key sentences from the alerting article. Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macroaverage F1 score of 93%. Moreover, it can successfully extract the relevant correct information on dengue fever from a ProMED-mail alerting article, which can help researchers or general users to quickly understand the essence of the alerting article at first glance. In addition to verifying the model, we also recruited 3 professional experts and 2 students from related fields to participate in a satisfaction survey on the generated summaries, and the results show that 84% (63/75) of the summaries received high satisfaction ratings. The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze the syntactic, semantic, and contextual information in the text. It then exploits the derived information to identify crucial sentences in the ProMED-mail alerting article. The experiment results show that the proposed method is not only effective but also outperforms the compared methods. Our approach also demonstrates the potential for case summary generation from ProMED-mail alerting articles. In terms of practical application, when a new alerting article arrives, our method can quickly identify the relevant case information, which is the most critical part, to use as a reference or for further analysis.